Modelling report

Are there unconscious visual images in aphantasia? Development of an implicit priming paradigm

Author

Affiliation

Maël Delem

Study of Cognitive Mechanisms (EMC) Laboratory

Packages and setup

# Packages ----------------------------------------------------------------

# using a reproducible environment
renv::restore()

# the cmdstanr package for Bayesian modelling has to be installed manually
# install.packages(
#   "cmdstanr", 
#   repos = c('https://stan-dev.r-universe.dev', getOption("repos")))

# The cmdstan backend, if not already installed, has to be installed on your 
# computer first, outside of the project:
# check_cmdstan_toolchain() # check if RTools is setup
# nb_cores <- parallel::detectCores() - 1
# install_cmdstan(cores = nb_cores)

# pacman allows to check/install/load packages with a single call
# if (!require("pacman")) install.packages("pacman") # already in renv.lock
library("pacman")

# packages to load (and install if needed) -------------------------------
pacman::p_load(
  here,      # easy file paths
  see,       # theme_modern and okabeito palette
  report,    # reporting various info 
  labelled,  # labelled data
  quarto,
  # ---- Modelling
  easystats, # modelling package framework
  lme4,      # mixed-effects models
  car,       # companion to lme4
  simr,      # power analysis
  statmod,   # power analysis
  emmeans,   # post-hoc tests
  # ---- Bayesian modelling
  brms,      # Bayesian regression models
  tidybayes, # tidy output for brms
  bayesplot, # Bayesian visualisations
  cmdstanr,  # Stan interface
  
  # ---- Visualisations
  qqplotr,    # QQ plots
  scales,     # ggplot2 scales
  latex2exp,  # LaTeX expressions in ggplot2
  ggbeeswarm, # beeswarm plots
  ggpubr,     # publication-ready plots
  patchwork,  # combining plots
  # ---- Data wrangling
  readxl,
  openxlsx,
  tidyverse  # modern R ecosystem
)


# Custom functions shared across scripts ----------------------------------
source(here("scripts/_functions.R"))


# Global cosmetic theme ---------------------------------------------------

theme_set(theme_modern(base_size = 14)) # from see in easystats

# setting my favourite palettes as ggplot2 defaults
options( 
  ggplot2.discrete.colour   = scale_colour_okabeito,
  ggplot2.discrete.fill     = scale_fill_okabeito,
  ggplot2.continuous.colour = scale_colour_viridis_c,
  ggplot2.continuous.fill   = scale_fill_viridis_c
)


# Fixing a seed for reproducibility ---------------------------------------
set.seed(14051998)


# Adding all packages' citations to a .bib --------------------------------
knitr::write_bib(c(.packages()), file = here("bibliography/packages.bib"))

1 Questionnaires analyses

1.1 Group differences

Table

Questionnaire	yes	no	df	t	p
VVIQ	19.083 ± 0.913	54.188 ± 1.095	148	-21.24	8.65e-47
OSIQ_Object	23.924 ± 0.994	46.561 ± 1.192	148	-13.29	5.07e-27
OSIQ_Spatial	41.011 ± 0.865	45.452 ± 1.036	148	-3.40	8.58e-04
SUIS	17.335 ± 0.749	37.132 ± 0.898	148	-15.51	7.75e-33

Plot

df_q_norm |> 
  
  # -------------------------- Long format first
  pivot_longer(
    cols = c(VVIQ:SUIS),
    names_to = "Questionnaire",
    values_to = "Score"
  ) |>
  mutate(
    Questionnaire = fct_relevel(
      Questionnaire, 
      c("VVIQ", "SUIS", "OSIQ_Object", "OSIQ_Spatial")
    )) |> 
  
  # -------------------------- Plotting
  ggplot(aes(
    x = Questionnaire, 
    y = Score, 
    fill = aphantasia, 
    color = aphantasia
    )) +
  
  # accentuation of the median line at 0.5
  geom_hline(
    yintercept = .5,
    linetype = 1,
    color = "grey30",
    alpha = .2
  ) +
  
  # --------------------- Scores
  # violin shapes for the distributions and quantiles
  geom_violin(
    position = position_dodge(dw),
    alpha = .1,
    draw_quantiles = c(.25, .5, .75)
  ) +
  # points for means and lines for CIs
  geom_pointrange(
    data = norm_means,
    aes(
      x = Questionnaire,
      y = Mean,
      ymin = CI_low,
      ymax = CI_high,
      color = aphantasia
    ),
    size = .75,
    linewidth = 1,
    position = position_dodge(dw)
  ) +
  
  # --------------------- Stars and lines
  # stars for significance
  annotate(
    geom  = "text", label = "***", color = "black",
    x = 1, y = .95, size  = 10 
    ) +
  # dotted line that separates the different questionnaires
  geom_vline(
    aes(xintercept = stage(Questionnaire, after_scale = 1.5)),
    linetype = 3
  ) +
  # stars 
  annotate(
    geom  = "text", label = "***", color = "black",
    x = 2, y = .95, size  = 10 
    ) +
  # dotted line
  geom_vline(
    aes(xintercept = stage(Questionnaire, after_scale = 2.5)),
    linetype = 3
  ) +
  # stars
  annotate(
    geom  = "text", label = "***", color = "black",
    x = 3, y = .95, size  = 10 
    ) +
  # dotted line
  geom_vline(
    aes(xintercept = stage(Questionnaire, after_scale = 3.5)),
    linetype = 3
  ) +
  # stars
  annotate(
    geom  = "text", label = "***", color = "black",
    x = 4, y = .95, size  = 10 
    ) +
  
  # --------------------- Aesthetics
  scale_color_okabeito(name = "Group:  ", labels = c(" Control   ", " Aphantasia")) +
  scale_fill_okabeito(name = "Group:  ", labels  = c(" Control   ", " Aphantasia")) +
  scale_x_discrete(
    name = "",
    labels = c("VVIQ", "SUIS", "OSIQ-Object", "OSIQ-Spatial")
  ) +
  scale_y_continuous(
    name = "Standardized score",
    breaks = breaks_pretty(8)
  ) +
  theme(
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(),
    axis.text.x  = element_text(size = txt),
    axis.title.y = element_text(size = txt),
    legend.title = element_text(size = txt_legend),
    legend.text  = element_text(size = txt_legend),
    legend.position = "top"
    )

ggsave(here("figures/questionnaires.png"), dpi = 600)

Figure S1.1

1.2 Congruence effects correlations

Matrices

corr <- 
  df_questionnaires |> 
    select(aphantasia, "Implicit effect":"SUIS") |>
    correlation(method = "spearman", p_adjust = "fdr") 

corr

# Correlation Matrix (spearman-method)

Parameter1      |      Parameter2 |   rho |        95% CI |        S |         p
--------------------------------------------------------------------------------
Implicit effect | Explicit effect |  0.05 | [-0.11, 0.21] | 5.44e+05 | 0.571    
Implicit effect |            VVIQ |  0.21 | [ 0.05, 0.37] | 4.51e+05 | 0.016*   
Implicit effect |     OSIQ_Object |  0.26 | [ 0.10, 0.40] | 4.27e+05 | 0.003**  
Implicit effect |    OSIQ_Spatial |  0.05 | [-0.11, 0.21] | 5.44e+05 | 0.571    
Implicit effect |            SUIS |  0.27 | [ 0.11, 0.41] | 4.21e+05 | 0.002**  
Explicit effect |            VVIQ |  0.08 | [-0.09, 0.24] | 5.29e+05 | 0.427    
Explicit effect |     OSIQ_Object |  0.12 | [-0.04, 0.28] | 5.03e+05 | 0.194    
Explicit effect |    OSIQ_Spatial | -0.02 | [-0.18, 0.15] | 5.84e+05 | 0.831    
Explicit effect |            SUIS |  0.16 | [ 0.00, 0.32] | 4.82e+05 | 0.081    
VVIQ            |     OSIQ_Object |  0.76 | [ 0.68, 0.82] | 1.40e+05 | < .001***
VVIQ            |    OSIQ_Spatial |  0.28 | [ 0.13, 0.43] | 4.11e+05 | 0.001**  
VVIQ            |            SUIS |  0.84 | [ 0.79, 0.88] | 89975.33 | < .001***
OSIQ_Object     |    OSIQ_Spatial |  0.09 | [-0.07, 0.25] | 5.20e+05 | 0.341    
OSIQ_Object     |            SUIS |  0.82 | [ 0.75, 0.86] | 1.06e+05 | < .001***
OSIQ_Spatial    |            SUIS |  0.30 | [ 0.15, 0.45] | 3.99e+05 | < .001***

p-value adjustment method: Benjamini & Hochberg (1995)
Observations: 151

corr |> summary() |> plot() + 
  labs(title = "Correlation matrix, whole group (Spearman FDR corrected)")

# ggsave(here("figures/corr-mat-whole.png"), dpi = 600)

Figure S1.2

Plots

layout <- "
AAAABBBB
##CCCC##
"

# ((p_corr_vviq + p_corr_osiq) / p_corr_suis) +
(p_corr_vviq + p_corr_osiq + p_corr_suis) +
  plot_layout(design = layout, guides = "collect") &
  theme(
    legend.position = "top", 
    legend.text = element_text(size = 20),
    legend.title = element_blank(),
    axis.title = element_text(size = 16)
    )

# ggsave(here("figures/corr-plots.png"), dpi = 600)

Figure S1.3

2 Accuracy analyses

We quickly checked for accuracy differences between groups with a logistic regression model.

fit_acc_im <- 
  glmer(
    formula = correct_implicit ~ (aphantasia + congruence + color)^3 + (1 | subjectid),
    data = df_i_acc,
    family = binomial(link = "logit"),
  control = glmerControl(optimizer = "bobyqa")
  )

fit_acc_ex <- 
  glmer(
    formula = correct_explicit ~ (aphantasia + congruence + color)^3 + (1 | subjectid),
    data = df_e_acc,
    family = binomial(link = "logit"),
    control = glmerControl(optimizer = "bobyqa")
  )

Table S2.1: Performance and estimates of the logistic GLMM fitted on accuracy in the implicit task.

AIC	AICc	BIC	R2 (cond.)	R2 (marg.)	ICC	RMSE	Sigma	Log_loss	Score_log
2614.99	2615.01	2679.02	0.17	9.48e-03	0.16	0.18	1.00	0.13	-Inf

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: correct_implicit
                             Chisq Df Pr(>Chisq)  
aphantasia                  1.1704  1    0.27932  
congruence                  0.2858  1    0.59296  
color                       6.3683  1    0.01162 *
aphantasia:congruence       0.2986  1    0.58479  
aphantasia:color            0.5591  1    0.45463  
congruence:color            0.0163  1    0.89851  
aphantasia:congruence:color 0.5143  1    0.47327  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S2.2: Estimated probability of a correct answer and log-odds contrasts between groups for the accuracy in the implicit task.

Estimated Marginal Means

color	Probability	SE	95% CI
Uncoloured	0.97	3.37e-03	(0.96, 0.98)
Coloured	0.98	2.70e-03	(0.97, 0.98)

Marginal means estimated at color

Marginal Contrasts Analysis

Level1	Level2	Odds ratio	95% CI	SE	df	z	p
Uncoloured	Coloured	0.73	(0.58, 0.92)	0.09	Inf	-2.63	0.009

Marginal contrasts estimated at color p-values are uncorrected.

Table S2.3: Performance and estimates of the logistic GLMM fitted on accuracy in the explicit task.

AIC	AICc	BIC	R2 (cond.)	R2 (marg.)	ICC	RMSE	Sigma	Log_loss	Score_log
2020.26	2020.28	2084.03	0.18	0.03	0.16	0.15	1.00	0.11	-Inf

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: correct_explicit
                              Chisq Df Pr(>Chisq)    
aphantasia                   1.5712  1     0.2100    
congruence                   0.2753  1     0.5998    
color                       18.9615  1  1.334e-05 ***
aphantasia:congruence        0.0186  1     0.8916    
aphantasia:color             0.6236  1     0.4297    
congruence:color             1.1352  1     0.2867    
aphantasia:congruence:color  0.2825  1     0.5951    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S2.4: Estimated probability of a correct answer and log-odds contrasts between groups for the accuracy in the explicit task.

Estimated Marginal Means

color	Probability	SE	95% CI
Uncoloured	0.97	3.15e-03	(0.97, 0.98)
Coloured	0.99	1.99e-03	(0.98, 0.99)

Marginal means estimated at color

Marginal Contrasts Analysis

Level1	Level2	Odds ratio	95% CI	SE	df	z	p
Uncoloured	Coloured	0.52	(0.39, 0.69)	0.08	Inf	-4.49	< .001

Marginal contrasts estimated at color p-values are uncorrected.

3 Response Times analyses

3.1 Rationale

To account for the non-normal, positively skewed distributions of the RTs, we fitted Generalized Linear Mixed Models (GLMMs) with inverse Gaussian distributions. The models were implemented in the lme4 R package and integrated in tidymodels workflows using the package multilevelmod. Models with Gamma and Gaussian distributions were also fitted and compared with the AIC and BIC to ensure that we chose the best distribution available.

The models included the Group (aphantasic, control), Congruence condition (congruent or incongruent) and Color condition (color or uncolored) along with all their two and three way interactions as fixed categorical predictors, while participants have been included as grouping factors (i.e. “random effects”). The random effect structure was chosen by fitting and comparing models with every possible combination of distribution and structure (intercept by participant, congruence or color, slope by participant on congruence and/or color) aiming for the best balance between goodness of fit and parsimony. Complex random-effects structures including various slopes on the factors failed to converge to stable and reliable estimates, hence the optimal models chosen included a single by-participant random intercept.

3.2 Model fitting

The formula of the model fitted is \(RT \sim (aphantasia + congruence + color)^3 + (1|subjectid)\).

model_formula <- rt ~ (aphantasia + congruence + color)^3 + (1|subjectid)

fit_implicit <- 
  glmer(
    formula = model_formula,
    data = df_i_rt,
    family = inverse.gaussian(link = "identity"),
    control = glmerControl(optimizer = "bobyqa")
  )

fit_explicit <- 
  glmer(
    formula = model_formula,
    data = df_e_rt,
    family = inverse.gaussian(link = "identity"),
    control = glmerControl(optimizer = "bobyqa")
  )

3.3 Model diagnostics

The quality checks of the models are displayed in Figure S3.1 and Figure S3.2 .

Implicit task

# characteristics to check
model_checks <- c("pp_check","homogeneity", "vif", "outliers", "qq", "reqq")

check_model(
  fit_implicit, 
  checks = model_checks, 
  detrend = FALSE, 
  residual_type = "normal"
  ) |> 
  plot()

# ggsave(here("figures/checks-implicit.png"), dpi = 600)

Figure S3.1: Model assumption checks for the Generalized Linear Mixed Model fit on the RTs in the implicit task.

Explicit task

check_model(
  fit_explicit, 
  checks = model_checks, 
  detrend = FALSE, 
  residual_type = "normal"
  ) |> 
  plot()

# ggsave(here("figures/checks-explicit.png"), dpi = 600)

Figure S3.2: Model assumption checks for the Generalized Linear Mixed Model fit on the RTs in the explicit task.

3.4 Model summaries

Table S3.1: Performance and estimates of the GLMM fitted on the implicit task data.

AIC	AICc	BIC	R2 (cond.)	R2 (marg.)	ICC	RMSE	Sigma
-4619.06	-4619.03	-4548.26	0.01	3.47e-04	0.01	0.26	0.48

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: rt
                             Chisq Df Pr(>Chisq)   
aphantasia                  0.6079  1   0.435588   
congruence                  8.6040  1   0.003354 **
color                       0.4346  1   0.509730   
aphantasia:congruence       8.5799  1   0.003399 **
aphantasia:color            0.0021  1   0.963046   
congruence:color            2.1571  1   0.141914   
aphantasia:congruence:color 1.4468  1   0.229035   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Table S3.2: Performance and estimates of the GLMM fitted on the explicit task data.

AIC	AICc	BIC	R2 (cond.)	R2 (marg.)	ICC	RMSE	Sigma
-2673.61	-2673.58	-2603.01	0.03	7.96e-04	0.03	0.28	0.41

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: rt
                              Chisq Df Pr(>Chisq)    
aphantasia                   0.0769  1  0.7815503    
congruence                  14.5055  1  0.0001398 ***
color                       47.6344  1  5.136e-12 ***
aphantasia:congruence       10.6801  1  0.0010830 ** 
aphantasia:color             0.8183  1  0.3656672    
congruence:color             0.2153  1  0.6426749    
aphantasia:congruence:color  0.7010  1  0.4024535    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3.5 Estimated means and contrasts

Table S3.3: Estimated means for each group in each congruence condition and contrasts between groups and conditions in the implicit task.

Group	Condition	Median (ms)	SE	df	asymp.LCL	asymp.UCL
Control	Incongruent	669.71	19.36	Inf	631.77	707.65
Aphantasia	Incongruent	676.89	15.84	Inf	645.84	707.93
Control	Congruent	644.38	19.31	Inf	606.53	682.22
Aphantasia	Congruent	675.05	15.83	Inf	644.02	706.08

Marginal Contrasts Analysis

Level1	Level2	Difference	95% CI	SE	df	z	p
Aphantasia Incongruent	Aphantasia Congruent	1.84e-03	(-0.01, 0.01)	5.23e-03	Inf	0.35	0.725
Aphantasia Incongruent	Control Congruent	0.03	(-0.02, 0.08)	0.02	Inf	1.30	0.193
Control Congruent	Aphantasia Congruent	-0.03	(-0.08, 0.02)	0.02	Inf	-1.23	0.219
Control Incongruent	Aphantasia Congruent	-5.34e-03	(-0.05, 0.04)	0.02	Inf	-0.21	0.831
Control Incongruent	Aphantasia Incongruent	-7.18e-03	(-0.06, 0.04)	0.02	Inf	-0.29	0.774
Control Incongruent	Control Congruent	0.03	( 0.01, 0.04)	6.13e-03	Inf	4.13	< .001

Marginal contrasts estimated at aphantasia, congruence p-values are uncorrected.

Table S3.4: Estimated means for each group in each congruence condition and contrasts between groups and conditions in the explicit task.

Group	Condition	Median (ms)	SE	df	asymp.LCL	asymp.UCL
Control	Incongruent	801.78	33.22	Inf	736.68	866.88
Aphantasia	Incongruent	799.21	27.05	Inf	746.18	852.23
Control	Congruent	770.29	33.17	Inf	705.28	835.30
Aphantasia	Congruent	794.57	27.04	Inf	741.56	847.57

Marginal Contrasts Analysis

Level1	Level2	Difference	95% CI	SE	df	z	p
Aphantasia Incongruent	Aphantasia Congruent	4.64e-03	(-0.01, 0.02)	5.45e-03	Inf	0.85	0.394
Aphantasia Incongruent	Control Congruent	0.03	(-0.05, 0.11)	0.04	Inf	0.68	0.499
Control Congruent	Aphantasia Congruent	-0.02	(-0.11, 0.06)	0.04	Inf	-0.57	0.570
Control Incongruent	Aphantasia Congruent	7.21e-03	(-0.08, 0.09)	0.04	Inf	0.17	0.866
Control Incongruent	Aphantasia Incongruent	2.57e-03	(-0.08, 0.09)	0.04	Inf	0.06	0.952
Control Incongruent	Control Congruent	0.03	( 0.02, 0.04)	6.42e-03	Inf	4.91	< .001

Marginal contrasts estimated at aphantasia, congruence p-values are uncorrected.

3.6 Visualisations

The figures below are also displayed in the main article.

subj_means_im <-
  df_i_rt |> 
  group_by(subjectid, aphantasia, congruence) |>
  reframe(rt = mean(rt)*1000)

(
  plot_models_full(subj_means_im, "aphantasia", emmeans_im, y_min = 400, y_max = 1110, size = 4) +
  plot_models_zoomed(subj_means_im, "aphantasia", emmeans_im, size = 4) &
  theme(
    legend.position = "top",
    legend.title = element_blank(),
    legend.text = element_text(size = 20),
    legend.margin = margin(0, 150, 25, 0),
    axis.title.y = element_text(size = 20),
    axis.text.x = element_text(size = 18)
  )
) +
  plot_layout(
    guides = "collect",
    widths = c(1, 1.2)
  )

# ggsave(here("figures/model-implicit.png"), dpi = 600)

Figure 3.1: Subject means and model-estimated means per group and condition in the implicit task.

subj_means_ex <-
  df_e_rt |> 
  group_by(subjectid, aphantasia, congruence) |>
  reframe(rt = mean(rt)*1000)

(
  plot_models_full(subj_means_ex, "aphantasia", emmeans_ex, y_min = 350, y_max = 1450, size = 4) +
  plot_models_zoomed(subj_means_ex, "aphantasia", emmeans_ex, size = 4) &
  theme(
    legend.position = "top",
    legend.title = element_blank(),
    legend.text = element_text(size = 20),
    legend.margin = margin(0, 150, 25, 0),
    axis.title.y = element_text(size = 20),
    axis.text.x = element_text(size = 18)
  )
) +
  plot_layout(
    guides = "collect",
    widths = c(1, 1.2)
  )

# ggsave(here("figures/model-explicit.png"), dpi = 600)

Figure 3.2: Subject means and model-estimated means per group and condition in the explicit task.

4 Finer sub-groups

df_i_acc |> 
  select(subjectid, aphantasia, sub_group) |> 
  distinct() |> 
  group_by(aphantasia, sub_group) |> 
  count() |> 
  arrange(sub_group) |> 
  rename(Aphantasia = 1, "Sub-group" = 2, N = 3) |> 
  display()

Aphantasia	Sub-group	N
Control	Hyperphantasia	1
Control	Control	57
Aphantasia	Hypophantasia	37
Aphantasia	Aphantasia	48

Interestingly, we have 39 participants that did not score at floor VVIQ but between 17 and 32, a score range that Reeder & Pounder (2024) have proposed to call “hypophantasia”. However, our sample comprised only 2 hyperphantasics, i.e. participants scoring above 74 (Zeman et al., 2020). These two participants will therefore be removed from the sample before performing new analyses on the aphantasic, hypophantasic and control groups.

4.1 Questionnaires analyses

Table

Questionnaire	Control	Hypophantasia	Aphantasia
VVIQ	53.492 ± 1.016	22.923 ± 1.26	16.049 ± 1.117
OSIQ_Object	46.09 ± 1.192	25.041 ± 1.478	23.04 ± 1.31
OSIQ_Spatial	45.467 ± 1.045	42.98 ± 1.295	39.456 ± 1.149
SUIS	36.983 ± 0.893	18.895 ± 1.107	16.102 ± 0.981

Level1	Level2	Difference	CI	SE	t(145)	p	Questionnaire
Control	Aphantasia	37.44	(34.45, 40.44)	1.51	24.73	< .001	VVIQ
Control	Hypophantasia	30.57	(27.37, 33.76)	1.62	18.91	< .001	VVIQ
Hypophantasia	Aphantasia	6.87	( 3.54, 10.21)	1.69	4.07	< .001	VVIQ
Control	Aphantasia	23.05	(19.54, 26.56)	1.78	12.98	< .001	OSIQ_Object
Control	Hypophantasia	21.05	(17.30, 24.80)	1.90	11.10	< .001	OSIQ_Object
Hypophantasia	Aphantasia	2.00	(-1.91, 5.91)	1.98	1.01	0.314	OSIQ_Object
Control	Aphantasia	6.01	( 2.93, 9.09)	1.56	3.86	< .001	OSIQ_Spatial
Control	Hypophantasia	2.49	(-0.80, 5.77)	1.66	1.50	0.137	OSIQ_Spatial
Hypophantasia	Aphantasia	3.52	( 0.09, 6.95)	1.74	2.03	0.044	OSIQ_Spatial
Control	Aphantasia	20.88	(18.25, 23.51)	1.33	15.70	< .001	SUIS
Control	Hypophantasia	18.09	(15.28, 20.89)	1.42	12.74	< .001	SUIS
Hypophantasia	Aphantasia	2.79	(-0.14, 5.72)	1.48	1.88	0.062	SUIS

Plot

# A tibble: 12 × 6
   Questionnaire sub_group          Mean     SE  CI_low CI_high
   <fct>         <fct>             <dbl>  <dbl>   <dbl>   <dbl>
 1 VVIQ          Control        5.86e- 1 0.0158  0.555   0.617 
 2 VVIQ          Hypophantasia  1.09e- 1 0.0196  0.0698  0.147 
 3 VVIQ          Aphantasia    -4.44e-16 0.0173 -0.0342  0.0342
 4 OSIQ_Object   Control        5.18e- 1 0.0198  0.479   0.557 
 5 OSIQ_Object   Hypophantasia  1.68e- 1 0.0245  0.119   0.216 
 6 OSIQ_Object   Aphantasia     1.34e- 1 0.0217  0.0909  0.176 
 7 OSIQ_Spatial  Control        5.09e- 1 0.0174  0.474   0.543 
 8 OSIQ_Spatial  Hypophantasia  4.68e- 1 0.0216  0.425   0.510 
 9 OSIQ_Spatial  Aphantasia     4.05e- 1 0.0191  0.368   0.443 
10 SUIS          Control        5.22e- 1 0.0186  0.485   0.558 
11 SUIS          Hypophantasia  1.45e- 1 0.0230  0.0992  0.190 
12 SUIS          Aphantasia     8.33e- 2 0.0203  0.0431  0.124 

df_q_norm |> 
  filter(sub_group != "Hyperphantasia") |>
  
  # --------------------- Long format first
  pivot_longer(
    cols = c(VVIQ:SUIS),
    names_to = "Questionnaire",
    values_to = "Score"
  ) |>
  mutate(
    Questionnaire = fct_relevel(
      Questionnaire, 
      c("VVIQ", "SUIS", "OSIQ_Object", "OSIQ_Spatial")
    )) |> 
  
  # --------------------- Plotting 
  ggplot(aes(
    x = Questionnaire, 
    y = Score, 
    fill = sub_group, 
    color = sub_group
    )) +
  
  # accentuation of the median line at 0.5
  geom_hline(
    yintercept = .5,
    linetype = 1,
    color = "grey30",
    alpha = .2
  ) +
  
  # ---------------- Scores
  # violin shapes for the distributions and quantiles
  geom_violin(
    position = position_dodge(dw),
    alpha = .1,
    draw_quantiles = c(.25, .5, .75)
  ) +
  # points for means and lines for CIs
  geom_pointrange(
    data = norm_means_finer,
    aes(
      x = Questionnaire,
      y = Mean,
      ymin = CI_low,
      ymax = CI_high,
      color = sub_group
    ),
    size = .75,
    linewidth = 1,
    position = position_dodge(dw)
  ) +
  
  # ---------------- Lines
  geom_vline(
    aes(xintercept = stage(Questionnaire, after_scale = 1.5)),
    linetype = 3
  ) +
  geom_vline(
    aes(xintercept = stage(Questionnaire, after_scale = 2.5)),
    linetype = 3
  ) +
  # dotted line
  geom_vline(
    aes(xintercept = stage(Questionnaire, after_scale = 3.5)),
    linetype = 3
  ) +
  
  # --------------- Significance
  # VVIQ control vs others
  plot_significance_labels(label = "***", size = 7,
    x_star = 0.83, y_star = .93,
    x_line = 0.66, x_line_end = 1,
    y_line = .92
  ) +
  geom_segment(
    x = 0.66, xend = 1.34, 
    y = .91, yend = .91, linewidth = .5, color = "black") +
  # VVIQ hypo vs aph
  plot_significance_labels(label = "***", size = 7,
    x_star = 1.17, y_star = .39,
    x_line = 1, x_line_end = 1.34,
    y_line = .38
  ) +
  
  # SUIS control vs others
  plot_significance_labels(label = "***", size = 7,
    x_star = 1.83, y_star = .93,
    x_line = 1.66, x_line_end = 2,
    y_line = .92
  ) +
  geom_segment(
    x = 1.66, xend = 2.34, 
    y = .91, yend = .91, linewidth = .5, color = "black") +
  # SUIS hypo vs aph
  plot_significance_labels(label = "*", size = 7,
    x_star = 2.17, y_star = .39,
    x_line = 2, x_line_end = 2.34,
    y_line = .38
  ) +
  
  # OSIQ-O control vs others
  plot_significance_labels(label = "***", size = 7,
    x_star = 2.83, y_star = .93,
    x_line = 2.66, x_line_end = 3,
    y_line = .92
  ) +
  geom_segment(
    x = 2.66, xend = 3.34, 
    y = .91, yend = .91, linewidth = .5, color = "black") +
  
  # OSIQ-S control vs aph only
  plot_significance_labels(label = "***", size = 7,
    x_star = 4, y_star = .84,
    x_line = 3.66, x_line_end = 4.34,
    y_line = .83
  ) +
  # OSIQ-S hypo vs aph
  plot_significance_labels(label = "*", size = 7,
    x_star = 4.17, y_star = .78,
    x_line = 4, x_line_end = 4.34,
    y_line = .77
  ) +
  
  # ----------------------------- Aesthetics 
  scale_color_okabeito(name = "Group:  ", labels = c(" Control   ", " Hypophantasia   ", " Aphantasia")) +
  scale_fill_okabeito(name = "Group:  ", labels  = c(" Control   ", " Hypophantasia   ", " Aphantasia")) +
  scale_x_discrete(
    name = "",
    labels = c("VVIQ", "SUIS", "OSIQ-Object", "OSIQ-Spatial"),
    expand = expansion(mult = c(0, 0))
  ) +
  scale_y_continuous(
    name = "Standardized score",
    breaks = breaks_pretty(8),
    expand = expansion(mult = c(0.05, 0.1))
  ) +
  theme(
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(),
    axis.text.x  = element_text(size = txt),
    axis.title.y = element_text(size = txt),
    legend.title = element_text(size = txt_legend),
    legend.text  = element_text(size = txt_legend),
    legend.position = "top"
    )

ggsave(here("figures/questionnaires-finer.png"), dpi = 600)

Figure S4.1

4.2 Response Times analysis

4.3 Model fitting

model_formula_finer <- rt ~ (sub_group + congruence + color)^3 + (1|subjectid)

fit_finer_i <- 
  glmer(
    formula = model_formula_finer,
    data = df_i_finer,
    family = inverse.gaussian(link = "identity"),
    control = glmerControl(optimizer = "bobyqa")
  )

fit_finer_e <-
  glmer(
    formula = model_formula_finer,
    data = df_e_finer,
    family = inverse.gaussian(link = "identity"),
    control = glmerControl(optimizer = "bobyqa")
  )

4.4 Model diagnostics

# characteristics to check
model_checks <- c("pp_check","homogeneity", "vif", "outliers", "qq", "reqq")

check_model(
  fit_finer_i, 
  checks = model_checks, 
  detrend = FALSE, 
  residual_type = "normal"
  ) |> 
  plot()

# ggsave(here("figures/checks-finer-implicit.png"), dpi = 600)

check_model(
  fit_finer_e, 
  checks = model_checks, 
  detrend = FALSE, 
  residual_type = "normal"
  ) |> 
  plot()

# ggsave(here("figures/checks-finer-explicit.png"), dpi = 600)

Figure S4.2: Model assumption checks for the Generalized Linear Mixed Model fit on the RTs in the implicit task with finer sub-groups.

Figure S4.3: Model assumption checks for the Generalized Linear Mixed Model fit on the RTs in the explicit task with finer sub-groups.

4.5 Model summaries

model_performance(fit_finer_i) |> display()

AIC	AICc	BIC	R2 (cond.)	R2 (marg.)	ICC	RMSE	Sigma
-4629.18	-4629.13	-4530.16	0.02	6.43e-04	0.01	0.26	0.48

# model_parameters(fit_finer_i) |> display()

Anova(fit_finer_i)

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: rt
                             Chisq Df Pr(>Chisq)   
sub_group                   1.4143  2   0.493042   
congruence                  8.7113  1   0.003162 **
color                       0.5361  1   0.464064   
sub_group:congruence       13.2326  2   0.001338 **
sub_group:color             1.2212  2   0.543029   
congruence:color            1.9224  1   0.165588   
sub_group:congruence:color  1.7589  2   0.415017   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

model_performance(fit_finer_e) |> display()

AIC	AICc	BIC	R2 (cond.)	R2 (marg.)	ICC	RMSE	Sigma
-2549.41	-2549.36	-2450.78	0.03	6.89e-04	0.03	0.28	0.41

# model_parameters(fit_finer_e) |> display()

Anova(fit_finer_e)

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: rt
                             Chisq Df Pr(>Chisq)    
sub_group                   0.0238  2  0.9881837    
congruence                 11.4280  1  0.0007234 ***
color                      44.0495  1  3.202e-11 ***
sub_group:congruence        8.1752  2  0.0167794 *  
sub_group:color             0.4794  2  0.7868834    
congruence:color            0.2415  1  0.6230973    
sub_group:congruence:color  2.2646  2  0.3222926    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

4.6 Estimated means and contrasts

Table S4.1: Marginal Contrasts Analysis

Condition	Sub-group	Median (ms)	SE	df	asymp.LCL	asymp.UCL
Incongruent	Control	667.63	19.48	Inf	629.45	705.82
Congruent	Control	642.01	19.43	Inf	603.92	680.10
Incongruent	Hypophantasia	668.39	24.03	Inf	621.28	715.49
Congruent	Hypophantasia	654.24	24.00	Inf	607.20	701.28
Incongruent	Aphantasia	682.92	20.92	Inf	641.92	723.92
Congruent	Aphantasia	691.12	20.93	Inf	650.10	732.13

Level1	Level2	Difference	95% CI	SE	df	z	p
Congruent Control	Congruent Aphantasia	-0.05	(-0.11, 0.01)	0.03	Inf	-1.72	0.085
Congruent Control	Congruent Hypophantasia	-0.01	(-0.07, 0.05)	0.03	Inf	-0.40	0.692
Congruent Control	Incongruent Aphantasia	-0.04	(-0.10, 0.01)	0.03	Inf	-1.43	0.152
Congruent Control	Incongruent Hypophantasia	-0.03	(-0.09, 0.03)	0.03	Inf	-0.85	0.393
Congruent Hypophantasia	Congruent Aphantasia	-0.04	(-0.10, 0.03)	0.03	Inf	-1.16	0.247
Congruent Hypophantasia	Incongruent Aphantasia	-0.03	(-0.09, 0.03)	0.03	Inf	-0.90	0.367
Incongruent Aphantasia	Congruent Aphantasia	-8.19e-03	(-0.02, 0.01)	7.04e-03	Inf	-1.16	0.244
Incongruent Control	Congruent Aphantasia	-0.02	(-0.08, 0.03)	0.03	Inf	-0.82	0.411
Incongruent Control	Congruent Control	0.03	( 0.01, 0.04)	6.15e-03	Inf	4.17	< .001
Incongruent Control	Congruent Hypophantasia	0.01	(-0.05, 0.07)	0.03	Inf	0.43	0.665
Incongruent Control	Incongruent Aphantasia	-0.02	(-0.07, 0.04)	0.03	Inf	-0.54	0.592
Incongruent Control	Incongruent Hypophantasia	-7.55e-04	(-0.06, 0.06)	0.03	Inf	-0.02	0.981
Incongruent Hypophantasia	Congruent Aphantasia	-0.02	(-0.09, 0.04)	0.03	Inf	-0.71	0.475
Incongruent Hypophantasia	Congruent Hypophantasia	0.01	( 0.00, 0.03)	7.79e-03	Inf	1.82	0.069
Incongruent Hypophantasia	Incongruent Aphantasia	-0.01	(-0.08, 0.05)	0.03	Inf	-0.46	0.648

Marginal contrasts estimated at congruence, sub_group p-values are uncorrected.

Table S4.2: Marginal Contrasts Analysis

Condition	Sub-group	Median (ms)	SE	df	asymp.LCL	asymp.UCL
Incongruent	Control	805.25	33.85	Inf	738.91	871.59
Congruent	Control	776.89	33.81	Inf	710.64	843.15
Incongruent	Hypophantasia	799.65	40.77	Inf	719.74	879.56
Congruent	Hypophantasia	796.06	40.76	Inf	716.17	875.95
Incongruent	Aphantasia	798.72	36.34	Inf	727.49	869.96
Congruent	Aphantasia	793.26	36.33	Inf	722.05	864.47

Level1	Level2	Difference	95% CI	SE	df	z	p
Congruent Control	Congruent Aphantasia	-0.02	(-0.11, 0.08)	0.05	Inf	-0.33	0.742
Congruent Control	Congruent Hypophantasia	-0.02	(-0.12, 0.08)	0.05	Inf	-0.36	0.717
Congruent Control	Incongruent Aphantasia	-0.02	(-0.12, 0.08)	0.05	Inf	-0.44	0.660
Congruent Control	Incongruent Hypophantasia	-0.02	(-0.13, 0.08)	0.05	Inf	-0.43	0.667
Congruent Hypophantasia	Congruent Aphantasia	2.81e-03	(-0.10, 0.11)	0.05	Inf	0.05	0.959
Congruent Hypophantasia	Incongruent Aphantasia	-2.66e-03	(-0.11, 0.10)	0.05	Inf	-0.05	0.961
Incongruent Aphantasia	Congruent Aphantasia	5.46e-03	(-0.01, 0.02)	7.44e-03	Inf	0.73	0.463
Incongruent Control	Congruent Aphantasia	0.01	(-0.09, 0.11)	0.05	Inf	0.24	0.809
Incongruent Control	Congruent Control	0.03	( 0.02, 0.04)	6.58e-03	Inf	4.31	< .001
Incongruent Control	Congruent Hypophantasia	9.19e-03	(-0.09, 0.11)	0.05	Inf	0.17	0.862
Incongruent Control	Incongruent Aphantasia	6.53e-03	(-0.09, 0.10)	0.05	Inf	0.13	0.895
Incongruent Control	Incongruent Hypophantasia	5.60e-03	(-0.10, 0.11)	0.05	Inf	0.11	0.916
Incongruent Hypophantasia	Congruent Aphantasia	6.39e-03	(-0.10, 0.11)	0.05	Inf	0.12	0.907
Incongruent Hypophantasia	Congruent Hypophantasia	3.59e-03	(-0.01, 0.02)	8.01e-03	Inf	0.45	0.654
Incongruent Hypophantasia	Incongruent Aphantasia	9.30e-04	(-0.11, 0.11)	0.05	Inf	0.02	0.986

Marginal contrasts estimated at congruence, sub_group p-values are uncorrected.

subj_means_finer_im <-
  df_i_finer |> 
  group_by(subjectid, sub_group, congruence) |>
  reframe(rt = mean(rt)*1000)

(
  plot_models_full(subj_means_finer_im, "sub_group", emmeans_finer_im, size = 4, dw = .6) +
  plot_models_zoomed(subj_means_finer_im, "sub_group", emmeans_finer_im, size = 4) &
  theme(
    legend.position = "top",
    legend.title = element_blank(),
    legend.text = element_text(size = 20),
    legend.margin = margin(0, 150, 25, 0),
    axis.title.y = element_text(size = 20),
    axis.text.x = element_text(size = 18)
  )
) +
  plot_layout(
    guides = "collect",
    widths = c(1, 1.2)
  )

# ggsave(here("figures/model-implicit-finer.png"), dpi = 600)

Figure 4.1

subj_means_finer_ex <-
  df_e_finer |> 
  group_by(subjectid, sub_group, congruence) |>
  reframe(rt = mean(rt)*1000)

(
  plot_models_full(subj_means_finer_ex, "sub_group", emmeans_finer_ex, y_min = 350, y_max = 1450, size = 4, dw = .6) +
  plot_models_zoomed(subj_means_finer_ex, "sub_group", emmeans_finer_ex, size = 4) &
  theme(
    legend.position = "top",
    legend.title = element_blank(),
    legend.text = element_text(size = 20),
    legend.margin = margin(0, 150, 25, 0),
    axis.title.y = element_text(size = 20),
    axis.text.x = element_text(size = 18)
  )
) +
  plot_layout(
    guides = "collect",
    widths = c(1, 1.2)
  )

# ggsave(here("figures/model-explicit-finer.png"), dpi = 600)

Figure 4.2

5 Bayesian modelling

We used Bayesian hypothesis testing to assess the congruence effect separately for each group. Thus, for each subset of the data (control, aphantasia, hypophantasia, aphantasia without hypo., control without hyperphantasia), we fitted a model with the formula \(RT \sim (congruence + color)^3 + (1|subjectid)\) and compared the models with and without the congruence effect using Bayes Factors.

formula <- bf(rt ~ congruence * color + (1 | subjectid))

We used more sensible priors than the default ones as regularizing priors to avoid overfitting.

# to see the default priors:
# get_prior(formula, data = df_i_rt, family = shifted_lognormal())
# need to define better priors for the intercept, sigma, b, and sd

prior <- c(
  prior(normal(-1, 0.5), class = 'Intercept'),  # around exp(-1) = 0.36 secs
  prior(normal(0.4, 0.3), class = 'sigma'), # SD of individual rts in log-units
  prior(normal(0, 0.3), class = 'b'), # around exp(-1) - exp(-1 + 0.3) = 150 ms 
  prior(normal(0.3, 0.1), class = 'sd')  # some variability between participants
)

Bayesian models were fitted using the brms package with the cmdstanr backend. We used parallel processing to fit the models. We aimed for 40000 post-warmup iterations to have sufficient draws to compute reliable Bayes Factors. Using a computer with 20 cores available for parallel processing, we could fit 20 chains of 3000 iterations with 1000 warmup iterations. Each model fitted in approximately 1 minute. Model fits are saved as .rds files for reproducibility and can be found on OSF in the analysis project, under the data/r-data-structures folder.

# detecting the number of cores to use
n_cores <- parallel::detectCores() - 4

# defining the number of iterations per chain (+ 1000 warm-up)
n_iter <- ceiling(40000 / n_cores) + 1000

# cmdstanr options
options(cmdstanr_warn_inits = FALSE)

# Implicit task, aphantasia group ---------------------------------------------
fit_implicit_aph <- 
  brm(
    formula = formula,
    data = df_i_rt |> filter(aphantasia == "Aphantasia"),
    family = shifted_lognormal(),
    prior = prior,
    sample_prior = TRUE,
    chains = n_cores,
    cores  = n_cores,
    iter   = n_iter,
    warmup = 1000,
    refresh = 5,
    backend = "cmdstanr",
    stan_model_args = list(stanc_options = list("O1")),
    save_pars = save_pars(all = TRUE),
    file = here("data/r-data-structures/brms-model-implicit-aph.rds"),
    file_compress = "xz"
  )

To save compilation time, we used the brms function update to use the first compiled model as a template for all the others.

# Implicit task models ----------------------------------------------------

# Control group ---------------------------------------------
fit_implicit_con <-
  # the `custom_update` function can be found in the `scripts/_functions.R` file
  custom_update(fit_implicit_aph,
    data = df_i_rt |> filter(aphantasia == "Control"),
    file = "data/r-data-structures/brms-model-implicit-con.rds"
  )

# Finer aphantasia group -------------------------------------
fit_implicit_finer_aph <- 
  custom_update(fit_implicit_aph,
    data = df_i_finer |> filter(sub_group == "Aphantasia"),
    file = "data/r-data-structures/brms-model-implicit-finer-aph.rds"
  )

# Hypophantasia group -------------------------------------
fit_implicit_hypo <- 
  custom_update(fit_implicit_aph,
    data = df_i_finer |> filter(sub_group == "Hypophantasia"),
    file = "data/r-data-structures/brms-model-implicit-hypo.rds"
  )

# Finer control group -------------------------------------
fit_implicit_finer_con <- 
  custom_update(fit_implicit_aph,
    data = df_i_finer |> filter(sub_group == "Control"),
    file = "data/r-data-structures/brms-model-implicit-finer-con.rds"
  )


# Explicit task models -----------------------------------------------------

# Aphantasia group ---------------------------------------------
fit_explicit_aph <- 
  custom_update(fit_implicit_aph,
    data = df_e_rt |> filter(aphantasia == "Aphantasia"),
    file = "data/r-data-structures/brms-model-explicit-aph.rds"
  )

# Control group ---------------------------------------------
fit_explicit_con <- 
  custom_update(fit_implicit_aph,
    data = df_e_rt |> filter(aphantasia == "Control"),
    file = "data/r-data-structures/brms-model-explicit-con.rds"
  )

# Finer aphantasia group -------------------------------------
fit_explicit_finer_aph <- 
  custom_update(fit_implicit_aph,
    data = df_e_finer |> filter(sub_group == "Aphantasia"),
    file = "data/r-data-structures/brms-model-explicit-finer-aph.rds"
  )

# Hypophantasia group -------------------------------------
fit_explicit_hypo <- 
  custom_update(fit_implicit_aph,
    data = df_e_finer |> filter(sub_group == "Hypophantasia"),
    file = "data/r-data-structures/brms-model-explicit-hypo.rds"
  )

# Finer control group -------------------------------------
fit_explicit_finer_con <- 
  custom_update(fit_implicit_aph,
    data = df_e_finer |> filter(sub_group == "Control"),
    file = "data/r-data-structures/brms-model-explicit-finer-con.rds"
  )

We finally compute Bayes Factor in favour of the null model of an absence of congruence effect for each group.

Implicit task models ------------------------------------------------

Aphantasia group, evidence against an implicit congruence effect: 
 BF_01 = 14.12439 

Control group, evidence against an implicit congruence effect: 
 BF_01 = 2.014823e-06 

Finer aphantasia group, evidence against an implicit congruence effect: 
 BF_01 = 12.59357 

Hypophantasia group, evidence against an implicit congruence effect: 
 BF_01 = 2.413134 

Finer control group, evidence against an implicit congruence effect: 
 BF_01 = 0.004105608 

Explicit task models ------------------------------------------------

Aphantasia group, evidence against an explicit congruence effect: 
 BF_01 = 12.08795 

Control group, evidence against an explicit congruence effect: 
 BF_01 = 0.002964782 

Finer aphantasia group, evidence against an explicit congruence effect: 
 BF_01 = 11.78268 

Hypophantasia group, evidence against an explicit congruence effect: 
 BF_01 = 9.81433 

Finer control group, evidence against an explicit congruence effect: 
 BF_01 = 2.179724e-06 

     

Session information for reproducibility

═════════════════════════════════════════════════════════════════════════

Analyses were conducted using the R Statistical language (version 4.4.1; R Core
Team, 2024) on Windows 11 x64 (build 22631)

Packages used:

  - quarto (version 1.4.4; Allaire J, Dervieux C, 2024)
  - qqplotr (version 0.0.6; Almeida A et al., 2018)
  - lme4 (version 1.1.35.5; Bates D et al., 2015)
  - Matrix (version 1.7.0; Bates D et al., 2024)
  - effectsize (version 0.8.9; Ben-Shachar MS et al., 2020)
  - brms (version 2.21.0; Bürkner P, 2017)
  - ggbeeswarm (version 0.7.2; Clarke E et al., 2023)
  - Rcpp (version 1.0.12; Eddelbuettel D et al., 2024)
  - car (version 3.1.2; Fox J, Weisberg S, 2019)
  - carData (version 3.0.5; Fox J et al., 2022)
  - cmdstanr (version 0.8.1; Gabry J et al., 2024)
  - bayesplot (version 1.11.1; Gabry J, Mahr T, 2024)
  - statmod (version 1.5.0; Giner G, Smyth GK, 2016)
  - simr (version 1.0.7; Green P, MacLeod CJ, 2016)
  - lubridate (version 1.9.3; Grolemund G, Wickham H, 2011)
  - ggpubr (version 0.6.0; Kassambara A, 2023)
  - tidybayes (version 3.0.6; Kay M, 2023)
  - labelled (version 2.13.0; Larmarange J, 2024)
  - emmeans (version 1.10.3; Lenth R, 2024)
  - parameters (version 0.22.0; Lüdecke D et al., 2020)
  - performance (version 0.12.0; Lüdecke D et al., 2021)
  - easystats (version 0.7.2; Lüdecke D et al., 2022)
  - see (version 0.8.4; Lüdecke D et al., 2021)
  - insight (version 0.20.1; Lüdecke D et al., 2019)
  - bayestestR (version 0.13.2; Makowski D et al., 2019)
  - modelbased (version 0.8.8; Makowski D et al., 2020)
  - report (version 0.5.8; Makowski D et al., 2023)
  - correlation (version 0.8.5; Makowski D et al., 2022)
  - latex2exp (version 0.9.6; Meschiari S, 2022)
  - here (version 1.0.1; Müller K, 2020)
  - tibble (version 3.2.1; Müller K, Wickham H, 2023)
  - datawizard (version 0.11.0; Patil I et al., 2022)
  - patchwork (version 1.2.0; Pedersen T, 2024)
  - R (version 4.4.1; R Core Team, 2024)
  - pacman (version 0.5.1; Rinker TW, Kurkiewicz D, 2018)
  - openxlsx (version 4.2.5.2; Schauberger P, Walker A, 2023)
  - ggplot2 (version 3.5.1; Wickham H, 2016)
  - forcats (version 1.0.0; Wickham H, 2023)
  - stringr (version 1.5.1; Wickham H, 2023)
  - tidyverse (version 2.0.0; Wickham H et al., 2019)
  - readxl (version 1.4.3; Wickham H, Bryan J, 2023)
  - dplyr (version 1.1.4; Wickham H et al., 2023)
  - purrr (version 1.0.2; Wickham H, Henry L, 2023)
  - readr (version 2.1.5; Wickham H et al., 2024)
  - scales (version 1.3.0; Wickham H et al., 2023)
  - tidyr (version 1.3.1; Wickham H et al., 2024)

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Figure S1.1: Figure S1.2: Figure S1.3: Figure S3.1: Model assumption checks for the Generalized Linear Mixed Model fit on the RTs in the implicit task. Figure S3.2: Model assumption checks for the Generalized Linear Mixed Model fit on the RTs in the explicit task. Figure 3.1: Subject means and model-estimated means per group and condition in the implicit task. Figure 3.2: Subject means and model-estimated means per group and condition in the explicit task. Figure S4.1: Figure S4.2: Model assumption checks for the Generalized Linear Mixed Model fit on the RTs in the implicit task with finer sub-groups. Figure S4.3: Model assumption checks for the Generalized Linear Mixed Model fit on the RTs in the explicit task with finer sub-groups. Figure 4.1: Figure 4.2:

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